Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

#install.packages("gganimate")
#install.packages("gifski")
#install.packages("gapminder")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(gganimate)
library(gifski)
library(gapminder)

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
gapminder %>% 
  filter(year== 1952) %>% 
  arrange(desc(gdpPercap)) %>% 
  head(1)
## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("1952")

ggplot(subset(gapminder, year == 1952), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +  # Gør punkterne lettere gennemsigtige
  scale_x_log10(labels = scales::label_comma()) +  # Brug log10 + normale tal på x-aksen
  scale_size(range = c(2, 10)) +  # Justér boblestørrelser
  labs(title = "GDP per Capita vs Life Expectancy (1952)", 
       x = "GDP per Capita (USD)", 
       y = "Life Expectancy (years)", 
       size = "Population",
       color = "Continent")  # Originale labels

…

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("2007")

ggplot(subset(gapminder, year == 2007), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::label_comma()) +
  scale_size(range = c(2, 10)) +
  labs(title = "GDP per Capita vs Life Expectancy (2007)", 
       x = "GDP per Capita (USD)", 
       y = "Life Expectancy (years)", 
       size = "Population",
       color = "Continent")

…

Questions for the static figures:

  1. Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result)

Fordi vi har en outlier, så bliver grafen meget meget svær at læse og clustered in, altså kompromeret. Derfor bruger man scale x log

  1. Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis?

ved at benytte chatgpt fandt jeg frem til formlen. gapminder %>% filter(year== 1952) %>% arrange(desc(gdpPercap)) %>% head(1) Det gav mig så svaret Kuwait

  1. Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)

Jeg har brugt ChatGPT til dele af følgende, og fundet frem til koden hvorved jeg har fixet grafen

ggplot(subset(gapminder, year == 1952), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + geom_point(alpha = 0.7) + # Gør punkterne lettere gennemsigtige scale_x_log10(labels = scales::label_comma()) + # Brug log10 + normale tal på x-aksen scale_size(range = c(2, 10)) + # Justér boblestørrelser labs(title = “GDP per Capita vs Life Expectancy (1952)”, x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size = “Population”, color = “Continent”) # Originale labels

ved figur 2 har jeg brugt denne ggplot(subset(gapminder, year == 2007), aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + geom_point(alpha = 0.7) + scale_x_log10(labels = scales::label_comma()) + scale_size(range = c(2, 10)) + labs(title = “GDP per Capita vs Life Expectancy (2007)”, x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size = “Population”, color = “Continent”)

  1. Answer: What are the five richest countries in the world in 2007?
top5_rigeste <- gapminder %>% 
  filter(year==2007) %>% 
  arrange(desc(gdpPercap)) %>% 
  head(5)

print(top5_rigeste)
## # A tibble: 5 × 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     2007    80.2   4627926    49357.
## 2 Kuwait        Asia       2007    77.6   2505559    47307.
## 3 Singapore     Asia       2007    80.0   4553009    47143.
## 4 United States Americas   2007    78.2 301139947    42952.
## 5 Ireland       Europe     2007    78.9   4109086    40676.

Dette giver mig så svaret på at top 5 rigeste lande er, 1: Norge, 2:Kuwait, 3:Singapore, 4:USA, 5:Irland udfra mit datasæt er dette min top 5.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2


library(ggplot2)
library(gganimate)
library(gapminder)
library(scales)  # For bedre aksemærkning
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
theme_set(theme_bw())  # Sæt et rent tema

# Animation med skiftende titel og forbedrede akser
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +  # Gør boblerne lettere gennemsigtige
  scale_x_log10(labels = label_comma()) +  # Fjern videnskabelig notation på x-aksen
  scale_size(range = c(2, 10)) +  # Justér boblestørrelser
  labs(title = "Year: {frame_time}",  # Dynamisk titel, der ændrer sig med årstallet
       x = "GDP per Capita (USD)", 
       y = "Life Expectancy (years)", 
       size = "Population",
       color = "Continent") +
  transition_time(year) +  # Animation med interpolering
  ease_aes('linear')  # Gør animationen glat

# Kør animationen
animate(anim2, renderer = gifski_renderer())

Tasks for the animations:

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

ChatGPT har hjulpet med at identificere transition_time(). Jeg har fundet frem til følgende svar, som jeg har smidt ind med spørgsmål 3 koden.

library(ggplot2) library(gganimate) library(gapminder) library(scales)

theme_set(theme_bw())

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) + geom_point(alpha = 0.7) +
scale_x_log10(labels = label_comma()) +
scale_size(range = c(2, 10)) +
labs(title = “Year: {frame_time}”,
x = “GDP per Capita (USD)”, y = “Life Expectancy (years)”, size = “Population”, color = “Continent”) + transition_time(year) +
ease_aes(‘linear’)

animate(anim2, renderer = gifski_renderer())

Jeg har så fået årstallet til at følge datasættet ved at benytte koden labs(title = “year:{frame_time}”)

  1. Can you made the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color
library(ggplot2)
library(gganimate)
library(gapminder)
library(scales)  # For bedre aksemærkning

theme_set(theme_bw())  # Sæt et rent tema for bedre synlighed

# Forbedret animation
anim6 <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +  # Let gennemsigtige bobler for bedre synlighed
  scale_x_log10(labels = scales::label_comma()) + # Fjern videnskabelig notation på x-aksen
  scale_size_continuous(labels = scales::label_comma()) + #Denne kode fjerner videnskabelig notation for befolkningstallet og viser det som tal med tusind-separatorer
  labs(title = "Year: {frame_time}",  # Dynamisk titel med årstal
       x = "GDP per Capita (USD)", 
       y = "Life Expectancy (years)", 
       size = "Population",
       color = "Continent") +
  theme(
    legend.position = "right",  # Placerer forklaringen til højre
    axis.text.x = element_text(angle = 45, hjust = 1),  # Roterer x-aksemærker for bedre læsbarhed
    axis.text.y = element_text(size = 10),  # Ændrer skriftstørrelse på y-aksen
    axis.title = element_text(size = 12),  # Ændrer skriftstørrelse på aksetitler
    legend.title = element_text(size = 12),  # Ændrer skriftstørrelse på legenden
    legend.text = element_text(size = 10)  # Ændrer skriftstørrelse på tekst i legenden
  ) +
  transition_time(year) +  # Animation over tid
  ease_aes('linear')  # Glidende overgang

# Kør animationen
animate(anim6, renderer = gifski_renderer())

Final Question

  1. Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]

Ved hjælp af ChatGPT

library(ggplot2)
library(dplyr)
library(gapminder)

# Filtrér data fra 2002 og 2007
data_comparison <- gapminder %>%
  filter(year %in% c(2002, 2007))

# Lav et sammenligningsplot
ggplot(data_comparison, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(aes(size = pop), alpha = 0.7) +
  scale_x_log10(labels = scales::label_comma()) +
  scale_size_continuous(labels = scales::label_comma()) + 
  labs(title = "Comparison of 2002 and 2007",
       x = "GDP per Capita (USD)", y = "Life Expectancy (Years)",
       color = "Continent", size = "Population") +
  facet_wrap(~year) +  # Opdel i to grafer (et for hvert år)
  theme_minimal()

data_2007 <- gapminder %>% 
  filter(year == 2002) %>%
  summarise(avg_gdp = mean(gdpPercap, na.rm = TRUE),
            avg_lifeExp = mean(lifeExp, na.rm = TRUE),
            total_pop = sum(pop, na.rm = TRUE))

print(data_2007)
## # A tibble: 1 × 3
##   avg_gdp avg_lifeExp  total_pop
##     <dbl>       <dbl>      <dbl>
## 1   9918.        65.7 5886977579
data_2007 <- gapminder %>% 
  filter(year == 2007) %>%
  summarise(avg_gdp = mean(gdpPercap, na.rm = TRUE),
            avg_lifeExp = mean(lifeExp, na.rm = TRUE),
            total_pop = sum(pop, na.rm = TRUE))

print(data_2007)
## # A tibble: 1 × 3
##   avg_gdp avg_lifeExp  total_pop
##     <dbl>       <dbl>      <dbl>
## 1  11680.        67.0 6251013179

Man kan så se at den generelle gdp er steget, så det generelle output pr person er steget, hvilket vil sige at man har fået flere penge mellem hænderne. Derudover kan man også se på den generelle levealder, hvor ud fra datasættet stiger med knap 1.5 år, hvilket er en stor forskel iforhold til hvis man kigger på tallene fra 1997 til 2002, hvor stigningen kun er 0.68 år. Dette viser at der er en stor udvikling i at mennesket lever længere generelt i verdenen. Til slut kan man så også se at befolkningstallet også er steget, hvilket giver mening i forhold til vi er blevet rigere og lever længere, hvilket giver god grund til hvorfor vi så bliver flere. Dog er uligheden stadig en udfordring. Mens nogle lande har oplevet stor vækst, er andre blevet efterladt, især i dele af Afrika, hvor økonomisk fremgang har været langsommere. Samlet set viser dataene, at verden i 2007 var rigere og sundere end i 2002. Der er stadig problemer, men udviklingen går i den rigtige retning.